Design and analysis issues in gene and environment studies

Abstract

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the “-omics” era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

An evaluation of the role of environmental factors in the disease penetrance of cervical dystonia

“…Although a case-sibling study design is at risk of over-matching because matched siblings share environmental exposures, such matching would obscure weak associations and highlight more influential associations 14. A case-sibling study is immune to population stratification bias15 and, for rare genes, it can lead to improved efficiency in the estimation of a gene-environment interaction 16…”

The importance of gene–environment interactions in human obesity

“…Despite the rapid growth of GEI studies in the field of obesity, there has been increasing scrutiny regarding the validity of these Biological model to explain gene-environment interactions in obesity studies based on several issues including statistical modelling [260,261], confounding [260,262], a low replication rate [263][264][265], underpowered analyses [266], lack of biological assumptions [267,268] and measurement precision [269] ( Table 1). The relevance of testing interactions between individual genetic variants and specific environmental exposures has also been questioned [260].…”

“…Evidence from toxicology research indicates that many environmental exposures display distinct dose-response curves that vary based on the developmental stage at which exposure occurs [304,305]. The identification of these developmental windows suggests a need to include time of exposure as a third interacting factor when analysing gene-environment interactions [268]. However, the inclusion of a three-way interaction term dramatically increases the necessary sample size [260,306] and this information is rarely available.…”

“…Simulation studies indicate that the sample size required to detect three-way interactions is 4-fold the sample size necessary to detect a two-way interaction of the same magnitude [307]. Another statistical method to address this issue involves considering environmental exposure as a time-varying factor to analyse the lag effects of gene × time-varying environment interactions [268]. Yet, the repeated measurements needed to measure lag effects are often not feasible due to the cost of repeated measurement in large studies.…”

“…Yet, the repeated measurements needed to measure lag effects are often not feasible due to the cost of repeated measurement in large studies. This constraint explains the high prevalence of cross-sectional case-control designs to study gene-environment interactions [268]. The challenge of measuring variations in the impact of environmental exposures is compounded by changes in the heritability of the outcome across time.…”